Automated self-service user support based on ontology analysis

ABSTRACT

A method for providing information to a user in response to a received user query. A natural language analysis generates substrings relevant to the user query. An ontology analysis outputs: terms of an ontology matching the relevant generated substrings; and relationships between the terms. A query analysis analyzes the user query regarding the outputted terms and relationships, including ascertaining whether the user query is more suitable for service than for an information search. If it is so ascertained, then service actions for the user to perform are identified to the user. If it is not so ascertained, then: the user query is refined based on the outputted terms and relationships; a search query is generated based on the refined user query, a search is initiated based on the search query, and results of the search are provided to the user.

TECHNICAL FIELD

The present invention relates generally to computer software, and moreparticularly, to automated self-service user support based on anontology of domain-specific information.

BACKGROUND

Automated self-service software applications are commonly deployed bybusiness enterprises to support customers with inquiries and problemsconcerning their products or services. Such applications may beintegrated with call center utilities to minimize the need for livesupport. Self-service support applications may use formal categories todescribe the domain in which anticipated inquiries and problems mayoccur, such as a domain on computer products or financial services. Theself-service support applications often assume that inquiry and problemcategories are well known and can easily be interpreted by the users.The applications may further assume that the formal categories haveuniversal definitions. However, these definitions are often dependent onthe underlying back-end support systems. In addition, most users wouldprefer to describe their problems or explain their needs in their ownterms, using free-form text. The user terms may not align with theformal problem categories or descriptions maintained by the user supportsystem.

For example, a user may explain a problem in the form of the statement“My laptop fails when I run program XYZ after I have started a backupusing program MNO”. Whereas, the user's company back-end technicalsupport systems are typically categorized using very specificterminology, e.g., laptop/desktop, operating system, CPU type,application, program, driver, storage, backup/restore, etc. Theself-service applications are thus less effective when their userinterfaces are based on system-centric terminology that does not matchwith the users' terminology.

Furthermore, even if the users know the specific system-centricterminology, they may not be able to formulate their questions to adegree where satisfactory results can be expected, unless they are awarewhat constitutes a complete description of a problem. This is due to thefact the users do not know the specific domain that support system usesand are not familiar with the terms, attributes, and relationships inthis domain.

There is thus a need for improved systems and processes for assistingusers to formulate self-service inquiries and effectively processingsuch self-service user inquiries.

BRIEF SUMMARY

The present invention provides a method for providing information to auser in response to a received user query, said method comprising:

a processor of a computer system receiving the user query from the user,said received user query expressed in a free-form text format;

said processor performing a natural language analysis to generatesubstrings relevant to the received user query;

after said performing the natural language analysis, said processorperforming an ontology analysis to output terms of an ontology ofdomain-specific information and to further output relationships betweenpairs of said terms, said outputted terms constrained to match therelevant substrings generated by said performing the natural languageanalysis;

after said performing the ontology analysis, said processor performing aquery analysis to analyze the user query with respect to the outputtedterms and relationships between the terms, said performing the queryanalysis comprising ascertaining whether the user query is more suitablefor service than for a search for information;

if said ascertaining ascertains that the user query is more suitable forservice than for said search for information, then identifying, to theuser, service actions to be performed by the user;

if said ascertaining ascertains that the user query is not more suitablefor service than for said search for information, then refining the userquery based on the outputted terms and relationships between the terms,generating a search query based on the refined user query, initiating asearch based on the search query, and providing results of the search tothe user.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of an exemplary user supportconfiguration in which aspects of the disclosure may be provided, inaccordance with embodiments of the present invention.

FIG. 2 illustrates a block diagram of a representative computer systemthat may be used in a computer-based user support system, such as thesupport configuration in FIG. 1, for providing aspects of the presentinvention.

FIG. 3 illustrates a block diagram of a self-service support system forreceiving and analyzing a user query based on an ontology ofdomain-specific information, and returning information relevant to theuser query, according to an embodiment of the present invention.

FIG. 4 illustrates in more detail another self-service support systemfor receiving and analyzing a user query based on an ontology ofdomain-specific information, and returning information relevant to theuser query, according to an embodiment of the present invention.

FIG. 5 illustrates an exemplary user interface menu for entering a userquery that may be processed by an embodiment of a self-service supportsystem of the present invention.

FIGS. 6-8 illustrate additional exemplary user interface menus that aself-service support system may present to the user to obtain moredetails about a user query in order to effectively process the query andidentify relevant results, in accordance with embodiments of the presentinvention.

FIG. 9 is a flowchart of a process for receiving and analyzing a userquery based on an ontology of domain-specific information, and returninginformation relevant to the user query, according to an embodiment ofthe present invention.

FIG. 10 is a flowchart of a process that a natural language processormay follow for analyzing a user query to extract relevant terms andparameters, and providing them to an ontology analyzer, according to anembodiment of the present invention.

FIG. 11 is a flowchart of a process that an ontology analyzer may followfor matching relevant terms from a user query against an ontology ofdomain-specific information to generate a search query for a searchengine, according to an embodiment of the disclosure.

FIG. 12 is a flowchart of a query refining process to determine thecompleteness and specificity of a user query, further refine the querywith user questions and answers, and determine follow-on actions,according to an embodiment of the present invention.

DETAILED DESCRIPTION

Exemplary embodiments of the disclosure relate to self-service usersupport applications that are based on the analysis of ontologies ondomain-related information. One aspect of the disclosure concerns asystem for providing information relating to a user query. The userquery may concern a problem that the user experienced with a product orservice, a question about a product or service, or other customer/userneeds. The system may comprise a natural language processor foridentifying relevant terms in the user query and an ontology analyzerfor matching the relevant terms to concepts in an ontology related tothe user query. The system may further include a query processor forrefining the user query using the matching relevant terms and ontologyconcepts, and a search engine for identifying from a databaseinformation relevant to the refined user query.

Another aspect of the disclosure concerns a computer implemented methodfor providing information relating to a user query. The method maycomprise parsing the user query to identify relevant terms from the userquery, matching the relevant terms to concepts in an ontology that arerelated to the user query, refining the query based on the matchingrelevant terms and ontology concepts, and searching a database forinformation relevant to the refined user query.

Still another aspect of the disclosure concerns a computer programproduct for providing information relating to a user query. The computerprogram product comprises a computer readable storage medium havingcomputer readable program code embodied therewith and configured toparse the user query to identify relevant terms from the user query, andmatch the relevant terms to concepts in an ontology that are related tothe user query. The program code may further be configured to refine theuser query based on the matching relevant terms and ontology concepts,and search a database for information relevant to the refined userquery.

Exemplary embodiments of the disclosure relate to self-service usersupport applications based on domain-specific information. As examples,the embodiments of the disclosure may be applicable to customer supportsystems in information technology (IT), financial services, health care,public sector information, legal services, education, and productmarketing, among others. The embodiments may be provided as stand-aloneproduct information or service support systems, or integrated with callcenter support applications. The embodiments allow a customer or user toenter a problem or need in free-form text. For example, in a financialservices environment, a customer may enter an inquiry in the form of“How to set up transfers from a bank account to a brokerage account?”

The embodiments may receive a user query, identify relevant terms anddetails from the user query, and if necessary, generate context-specificuser questions based on knowledge-driven understanding and intelligenceleveraged from the ontology. The query may be refined with user answersto additional questions presented to the user. This is an iterativeprocess in which the query may be re-processed based on the questionsand answers. The embodiments may use relevant terms and detailsextracted from the user query to analyze an ontology of domain-specificinformation and generate a search query. The embodiments may use thesearch query to identify information related to the user query fromavailable information repositories or suggest relevant follow-onactions, such as applicable tools or ancillary processes. Theinformation identified by the embodiments of the invention may includedocument titles, portions of documents, user manuals, web pages, tools,processes, and links to documents that are relevant to the user query.

Referring to FIG. 1, there is illustrated a block diagram of anexemplary computer configuration in which aspects of the disclosure maybe provided. Computer configuration 10 may include multiple clientcomputers 11-12 for accessing a server 13 to receive user supportthrough network 14. Server 13 may host a self-service user supportapplication 15 for assisting customers with their product information orservice problems, and providing answers to customer inquiries.

FIG. 2 illustrates a block diagram of a representative computer systemthat may be used in a user support configuration, such as theconfiguration 10 in FIG. 1, for providing aspects of the invention. Dataprocessing system 200 may include a processor unit 211, a memory unit212, a persistent storage 213, a communications unit 214, aninput/output unit 215, a display 216, and system bus 217. Computerprograms are typically stored in persistent storage 213 until they areneeded for execution, at which time the programs are brought into memoryunit 212 so that they can be directly accessed by processor unit 211.Processor 211 selects a part of memory 212 to read and/or write by usingan address processor 211 gives to memory 212 along with a request toread and/or write. Usually, the reading and interpretation of an encodedinstruction at an address causes processor 211 to fetch a subsequentinstruction, either at a subsequent address or some other address. Thus,a computer system of the present invention may comprise a processor(e.g., processor 211), a memory (e.g., memory 212) coupled to theprocessor, and a computer readable storage device (e.g., persistentstorage 213) coupled to the processor, said storage device containingprogram code configured to be executed by the processor via the memoryto implement the methods of the present invention.

FIG. 3 illustrates a block diagram of a self-service support system 300for receiving a user query, analyzing the query based on an ontology ofdomain-specific information, and returning information relevant to theuser query, according to an exemplary embodiment of the disclosure. Asexamples, the domain may concern financial services or customer supportfor IT products. The system 300 may be a software application operatingon server 13, and may comprise a user interface system 302 and a querylogic system 303. User interface system 302 allows a user 301 to enter,for example, a problem, user need, or inquiry about a supported productor service. The user's problem, need or inquiry may be in the form of auser query statement 304.

Once the user query statement 304 has been analyzed and processed by thequery logic system 303, the query logic system 303 may return to theuser 301 documents or other information 311 that the query logic system303 identifies as being relevant to the user query. The identifiedinformation 311 may be returned through user interface system 302.Exemplary user interface menus that the user interface system 302 maypresent to the user 301 are described below with reference to FIGS. 5-8.

The user interface system 302 may present the user with additionalquestions about the user query 304, as generated by the query logicsystem 303. The user interface system 302 may also forward user answersto the query logic system 303 during the processing of the user query. Afunction of the query logic system 303 may be to bridge between theuser's free-form description of the user query 304 and the fixedback-end categories based on the use of ontologies, which are analyzedto clarify the user query statement. The bridging process may includedisambiguation, augmentation, and extension of the user query 304, usinga domain-specific ontology, to improve specificity and completeness ofthe user query. The query logic system 303 may assess the resulting userquery against knowledge bases, tools, processes, or assets which maysupport the user's self-service request. The resulting query may befurther refined by a query processor based on questions for the user anduser answers.

An ontology is a data structure that formally represents concepts andassociated relationships in a technical support domain, e.g., banking,health care, computer products, etc. It may be used not only to definethe domain and provide a shared vocabulary, but to provide properties ofdata in the domain. In the embodiments of the invention, the ontologymay initially be prepared by domain and ontology specialists. The querylogic system 303 may refine and extend the ontology over time throughtext mining and ontology matching of additional user inputs, asdescribed below with reference to the ontology editor 409 and ontologybuilder 410 in FIG. 4, and the ontology analysis process in FIG. 11.

An ontology may comprise an ontology model and a knowledge store. Theontology model may be in the form of a Web Ontology Language (OWL) filecontaining the main domain concepts, which are relatively static. Theknowledge store may be in the Resource Description Format (RDF) andconform to the OWL file. As an example of a domain use, the ontologymodel may capture four main elements of a “perfect” or “complete” query,which may include information on: (1) what the user's need or problem is(e.g., situation, symptoms); (2) where the need or problem occurs (e.g.,affected components); (3) in what environment (e.g., affected product);and (4) what changed (e.g., user activity that led to the problem).

The query logic system 303 may deduce semantic meaning in the user queryand analyze it against a domain representation. The query logic system303 may present the user with a series of questions until a search querycan be generated that would return a reasonable amount of results. Thequery logic system 303 may further expose the user to the structure ofthe ontology through its questions and refine the user query based onuser answers to the questions. The refinement is not automated butrather involves the user. It allows a mixed-initiative interaction,where the user is contributing to the formulation of the refined queryby answering questions or providing additional information. The searchquery may be used to perform a meta-search, where it may be sent to asingle or multiple heterogeneous back-end databases, knowledge storesand available tools, through multiple search engines. The query logicsystem 303 may return relevant results in a unified, but categorized andfiltered by the query input, list of information.

In an exemplary embodiment of the invention, the query logic system 303may comprise a natural language processor 305, ontology analyzer 306,query processor 307, and search engine 308. The natural languageprocessor 305 may analyze a query entered by the user to extract keydetails from the user query. Query details may include, for example, thetype of user problem, what the user was doing when the problem occurred,the environment in which the problem occurred, affected productcomponents, and conditions that have changed as a result of the problem.The output from the natural language processor 305 may be in the form ofrelevant substrings (e.g., key terms) and annotations on the relevantsubstrings. The natural language processor 305 is described in detailbelow with reference to FIGS. 4 and 10.

An ontology analyzer 306 may receive relevant strings and annotationsfrom the natural language processor 305 for analyzing an ontology ofdomain-specific information related to the user query, and identifyingconcepts and relationships in the ontology that match the user's problemor need. The ontology analyzer 306 is described in detail below withreference to FIGS. 3-4 and 11. The query logic system 303 may furthercomprise a query processor 307 for refining the user query in terms ofcompleteness and specificity. As part of the user query refiningprocess, the query processor 307 may generate additional questions aboutthe user's problem or need that the user interface system 302 may askthe user, and process user answers to these questions. Details on thegeneration of user questions and the processing of user answers aredescribed below with reference to FIGS. 4 and 12.

The query processor 307 may further determine follow-on user serviceactions and present these service actions to the user, such assuggesting to the user to open a problem record or information request.As an output of the query refinement process, the query processor 307may generate more specific terms, phrases, and additional information(if missing) that more accurately describe the user's problem or need.The query processor 307 may then provide these terms, phrases, andadditional information to the search engine 308. The query processor 307is further described below with reference to FIGS. 4 and 12.

The search engine 308 may identify information relevant to the userquery 304 from databases 309 of product and service data, Internet andintranet 310, and other available repositories of information. The querylogic system 303 may return the identified information to user 301through user interface system 302. Search engine 308 may comprise a datasearch or data analytics program, such as Google™ search engine or IBMDB2 Intelligent Miner™.

FIG. 4 illustrates in more detail an exemplary embodiment of a querylogic system 400 for receiving and analyzing a user query based on anontology of domain-specific information, and returning relevantinformation to the user. The query logic system 400 may be implementedas layers where each layer is responsible for a set of relatedprocessing tasks. For example, a natural language processing layer 402may be responsible for parsing a user query in natural language, e.g.,English. The language processing layer 402 may include a naturallanguage processor 403 for breaking the user query into tokens or keywords relating to the problem, such as “failed”, “program”, “start up”,“hang”, etc. An example of the natural language processor 403 may be theIBM LanguageWare™ natural language processor. The natural languageprocessor 403 may further parse the key words into a formalrepresentation that is more readily utilized by a computer application.

In an embodiment of the invention, the natural language processor 403may perform a lexical analysis of the user's description of a problem orneed. It may initially parse the description into paragraphs, sentencesand tokens using a break-rules dictionary. It may look up a token in oneor more dictionaries to find out more information about a word, forexample, its part of speech (POS). The dictionaries may include bothstandard linguistic dictionaries containing all words in the selectedlanguage and custom dictionaries containing words from a specific domainof knowledge.

In addition, the natural language processor 403 may perform other typesof analysis to determine the nature, format and meaning of the textbeing processed. For example, the natural language processor 403 mayapply a language identification to a body of text to determine thelanguage in which it was written. Lexical analysis may be used toidentify words and their attributes as well as determine the part ofspeech (POS) of each word. Semantic analysis may be employed todetermine the contextual meaning of words and phrases, through anunderstanding of the grammatical structural patterns of a language usinga process of relating syntactic structures. Semantic analysis is a phaseof natural language processing, following parsing, that involvesextraction of context-independent aspects of a sentence's meaning,including the semantic roles of entities mentioned in the sentence, andquantification information, such as iteration and dependency.

As part of the natural language processing, natural language processor403 may further include functions for spell-checking, POSdisambiguation, normalization (i.e., determining the lemma or canonicalform of a word, which is also known as ‘morphological analysis’), andanaphora resolution. Normalization is the process of determining asingle string representation for a word or term found in text. Fornormalization of inflectional variance (run, running, runs, etc.), thisis traditionally called the lemma, citation form, or canonical form.Lemmatization uses morphological analysis of words to determine a singleword, called the lemma of the word, which groups together inflectedforms of the words by removing only inflectional endings of the words.Part of Speech (POS) is the linguistic category of a word, such as noun(the run), verb (to run), adjective (runny honey), etc. POSdisambiguation is the process of assigning the correct POS to a word andword sense (semantic) disambiguation is the process of identifying whichsense of a word is used in any given sentence when the word has a numberof distinct senses.

The natural language processor 403 may refer to a dictionary 404 toobtain the meaning of unfamiliar terms that the user enters. It may lookup a thesaurus 405 for synonyms, antonyms, etc., and a lexicon 406 forexpressions. A lexicon is a language's vocabulary that includes words aswell as common expressions. It is a language's inventory of lexemes. Thelexicon includes not only entries for words and phrases, but alsolexical relations, syntactic argument structures, and grammaticalrelations. During the processing of the user query by natural languageprocessor 403, the natural language processing layer 402 may extract keysubstrings from the user query and provide them to an ontology layer407. The ontology layer 407 may match these substrings against anontology of domain-specific information that is related to the user'sneed or problem.

As an example, the user may input a query as “Instollation problem onUNIX”. The natural language processing layer 402 may perform thefollowing tasks:

Identify the language of the text as English.

Recognize the misspelling of “installation” (Instollation).

Determine the canonical form of “installation” (install).

Recognize a technical support domain term (UNIX).

Semantic recognition of an incident (Installation problem).

Once the natural language process is competed, the ontology layer 407 ofthe query logic system 400 may begin analyzing the user query against arelated domain ontology. The query logic system 400 may iterativelyrefine the user query based on concepts and relationships that theontology layer 407 identifies from the ontology, with the goal ofincreasing the relevance of search results.

The ontology layer 407 may include an ontology concept matcher 408 forexamining terms and relationships in the ontology and matching themagainst the substrings that were extracted from the user query. Theontology may be visualized as a tree comprising nodes and edgesconnecting adjacent nodes, wherein each node in the tree is associatedwith a term, and wherein a connection between two nodes is an edgerepresenting a relationship between the terms associated with theconnected nodes. Based on the analysis of the ontology, the ontologyconcept matcher 408 may provide a set of terms from the ontology, andtheir relationships, that match the relevant substrings extracted fromthe user query. The matched terms and relationships may be forwarded tothe query processing layer 416 for continued processing by the querylogic system 400.

The ontology concept matcher 408 may match a token extracted from orgenerated from the user query to each concept in an ontology (e.g., anode in an ontology structure), and attributes and relationshipsassociated with the concept. In one embodiment, the token is a substringrelevant to the user query generated by the natural language processor.Attributes may include sub-components, acronyms, and synonyms of theconcept. If there is a match between the token and a concept (i.e.,term) in the ontology, the natural language processing layer 402 mayannotate the token (e.g., generated substring) with the matching concept(e.g., matching term represented by a node in the tree representing theontology) and its associated pillars. The associated pillars may includesituations, activities, products, IT components, etc., that minorrequirements of a “perfect” query.

If the ontology concept matcher 408 identifies a partial match between atoken and an ontology concept, the natural language processing layer 402may annotate the matching token, and if necessary the query logic system400 may confirm the partial ontology match with the user. In case theontology concept matcher 408 identifies multiple ontology concepts thatmatch a token, the query logic system 400 may ask the user to clarifyand select the best ontology match through questions for the user. Theuser can choose the correct words based on the context, pillar anddescription of the matching token and ontology concepts.

The ontology layer 407 may further comprise an ontology editor 409 andan ontology builder 410. The ontology editor 409 allows an ontologyspecialist to edit and create an ontology for a particular domain. Anexample ontology editor is the open-source Protege editor. Using theProtege editor, an ontology specialist can edit and create an ontologyin RDF and OWL script languages. The ontology builder 410 allows anontology to be updated with additional terms and relationships that thequery logic system 400 may identify while processing user queries. Theontology builder 410 thus extends the ontology and refines its contentsover time in terms of completeness and accuracy, based on actual userneeds and problems and information identified in response to userqueries.

Once the ontology-matching process is completed, the query logic system400 may forward matched ontology concepts to the query processing layer416 to assess whether the user query is complete and specific enough forprocessing or needs refinement with additional user input. The queryprocessing layer 416 may include a term checker 411 for determining thespecificity of the key terms extracted from the user query. For eachpart of the user query, the term checker 411 may determine whether thereturned ontology match is sufficiently specific for a search. If theontology match is not specific enough, the query processing layer 416may ask the user additional questions to improve the specificity of theontology match.

The query processing layer 416 may include a completeness checker 412 toassess whether the user query is sufficiently complete for processing.The completeness checker 412 may determine whether each part of a“perfect” query is satisfied. In an exemplary embodiment, completenessmay mean that the query logic system 400 has sufficient information toallow an expert in the field to respond to the user. For example, thequery logic system 400 may need information on: a) what the user wastrying to do, b) what problem the user encountered, and c) what productor service the user was using. All three aspects of the problemdescription would be needed to satisfy completeness. For any missingelements, the query processing layer 416 may ask the user furtherquestions with the goal of satisfying each part of a perfect query.

The query processing layer 416 may include a user question and answerprocessor 413 for generating user questions and obtaining additionaldetails about the user's problem or need. Information obtained from theuser questions and answers is used to refine the user query, asdescribed above. In an exemplary embodiment, the questions may addressareas that would help the system “understand” the problem better, suchas “what the problem is?”, “where did the problem occur?”, “in whatenvironment?”, and “what changed?”. The question and answer processor413 may not always ask all the questions. It may determine whichquestions to ask depending of the level of specificity and granularityof the user query, to allow the query logic system 400 to reasonablyreturn relevant results. The results may include suitable documents froma search on the user's need or problem in various domains, or relevantfollow-on actions such as applicable tools and services.

In an example embodiment, the user question and answer processor 413 maycombine information in the domain ontology with the user's specificproblem or need to explain a condition that might have caused the user'sproblem and to improve the user's trust and relationship. For example,in response to a customer's problem concerning a financial transaction,the question and answer processor 413 may inform the customer of arecent system upgrade, offer to assist the customer by telephone, andprovide incentives that may be of value to the customer.

The question and answer processor 413 may further tailor the questionsto display specific words that are appropriate for the user's situation.The questions may include substituted words to allow the system tointeract in the context relevant to the user. For example, the user maystate in the user query that “the notebook computer failed to boot”. Ingenerating questions for the user, the question and answer processor 413may substitute the word “laptop” in its repository of questions with theword “notebook”. Through refinement questions for the user, the questionand answer processor 413 can expose the ontology structure to the user,thereby allowing the user to learn more about the particular domain thatthe query logic system 400 is using. Based on the user's answers, thequestion and answer processor 413 may generate additional user questionsto further refine the user query.

The user question and answer interaction is thus an iterative processfor refining the user query with user input. The question and answerprocessor 413 may capture significant elements of the user query, butmay ask further questions and provide answer suggestions based on thedomain-specific ontology until the user query is close to a “perfect”query. The query logic system 400 is providing a learning experience tothe user to formulate better questions, while the user is potentiallyproviding extra, not yet captured domain-specific knowledge to be addedover time to the formal representation of the domain (ontology).Specifically, the question and answer processor 413 refines andoptimizes the user's free-form text entry with the purpose of describingthe user's need or problem with sufficient specificity and completeness.It leverages concepts and relationships from the ontology to determinethe questions and the sequence of presenting the questions to the user.

The query processing layer 416 may further include a previous querychecker 414 to determine whether the user query is similar to a querythat has previously been processed by the query logic system 400.Previously handled queries and their solutions may be maintained in aknowledge repository which the query logic system 400 can access. Forexample, the user query may concern a computer's failure to boot thatwas caused by the installation of a particular program, and this problemhas previously been processed by the query logic system 400 and storedin a database. The previous query checker 414 may look up informationrelating to this particular problem in the repository and respond to theuser with the identified procedure for fixing the problem, withoutre-processing the user query.

The query processing layer 416 may comprise other services 415 such asfunctions to determine follow-on actions for the user or request theuser to create a problem report, a purchase request, or an onlineprocess/activity. Other services 415 may determine that the user queryis more suitable for a service rather than a search. User services mayinclude analyzing system logs, cataloging symptoms, or checking thecompatibility of products, models, release versions, etc., for eithersales or support functions.

Once the query processing layer 416 has checked the completeness andspecificity of the user query, and refined the user query if necessary,it may send relevant key terms and relationships about the user's needor problem to a search layer 419. The search layer 419 may include asearch engine or data analytics program 417 with access to relevantinformation sources 418 such as databases of product documents. Thesearch engine or data analytics program 417 may also access the Internetor a company's intranet to search for information relevant to the query.The search layer 419 may employ a result-ranking utility for ranking theinformation sources that best match the key terms of the user query andreturning a set of best-matching results. The search layer 419 may thenreturn the best-matching results to the user through the user interface302.

FIG. 5 illustrates an exemplary user screen of a self-service supportsystem, e.g., system 300, to allow a user to enter a user query orquestion about products or services, according to an embodiment of theinvention. The user screen 501 may include a user interface component502 in which the user may enter a question or describe a service need orproblem. Upon selecting submit button 503, the self-service supportsystem 300 may display a follow-on screen, as shown in FIG. 6, which mayprompt the user for additional information about the user query. Forexample, if the user had entered “Change disk drive” in the userinterface component 502, then the support system 300 may ask the user toselect whether the disk drive is for a logical volume or a physicalvolume, as illustrated by user choices 604 in FIG. 6. In addition, theuser interface 302 may display a user interface component where the userdoes not have to select one of the system choices, but can enter theuser's own terms. These terms may then be used for extending theontology by the ontology builder 410.

The support system 300 may continue to prompt the user for additionaldetails about the user's need or problem by presenting other userscreens until it could determine that the description of the user queryis sufficiently complete and specific for a data search. For example, inthe disk drive change scenario, the support system 300 may ask the userto specify the type of disk drive change as illustrated by user question705 in FIG. 7. Once a query logic system 400 has a reasonably completedescription of the user's need or problem, its components would processthe user query, as described with reference to FIG. 4. The results froma search for information relevant to the user query may be displayed tothe user as document list 806 in FIG. 8.

FIG. 9 is a flowchart of a process for receiving and analyzing a userquery based on a domain-specific ontology, and returning informationrelevant to the user query, according to an embodiment of thedisclosure. The process may start at step 901 where a user enters a userquery or question in free-form text, such as the phrase “change diskdrive”.

In step 902, a natural language processor, such as natural languageprocessing layer 402 of the query logic system 400, generates substringsrelevant to the user query, such as by extracting key terms about theuser's need or problem from the user query, e.g., “change” and “diskdrive”. Generating the substrings may comprise utilizing a linguisticcategory assigned to different words of the user query and/or utilizinga single word consisting of a lemma that is specific to each word of aplurality of words of the user query

In step 903, an ontology analyzer performs an ontology analysis tooutput terms of an ontology of domain-specific information and furtheroutputs relationships between pairs of the terms. The outputted termsare constrained to match the relevant substrings so that thedomain-specific information of the ontology relates to the user query.Thus, the ontology layer 407 in the query logic system 400, may analyzea related domain ontology and match the extracted key terms againstconcepts and relationships in the ontology, at step 903.

In step 904, a query analysis is performed to analyze the user querywith respect to the outputted terms and relationships between the terms.If the query logic system 400 determines that the user query is notcomplete or specific enough for a search, then the query processinglayer 416 may generate additional questions about the user's need orproblem, per step 904. The query processing layer 416 may further refinethe user query, at step 905, to make it more complete and specific for asearch, based on the user's answers to these questions. The queryprocessing layer 416 may reprocess the refined user query as shown bythe loop back from step 905 to step 902.

The query analysis ascertains whether the user query is more suitablefor service than for a search for information. If it is ascertained bythe query analysis that the user query is more suitable for service thanfor a search for information, then service actions to be performed bythe user are identified to the user. If it is ascertained by the queryanalysis that the user query is not more suitable for service than for asearch for information, then: (i) the user query is refined based on theoutputted terms and relationships between the terms and/or answers toquestions (step 905); (ii) a search query is generated (step 906) basedon the refined user query; (iii) a search is initiated (step 907) basedon the search query; and (iv) results of the search are provided to theuser (step 908).

At step 906, the query processing layer 416 in the query logic system400 may generate a search query that includes search terms, concepts andannotations, based on the refined user query. The query logic system 400may provide the search query to a search layer 419 for identifyinginformation relevant to the user's problem or need, using the searchquery, at step 907. The search layer 419 may use a search engine or dataanalytics program 417 to search repositories 418 of product and serviceinformation. In addition to presenting the search results to the user atstep 908, the query logic system 400 may solicit user feedbackconcerning the relevancy of the resulting information, per step 909.

FIG. 10 is a flowchart of an exemplary process that a natural languageprocessing layer 402 may follow for analyzing a user query to extractrelevant terms and details about the user's need or problem, andproviding them to an ontology layer 407. The natural language processinglayer 402 may start at step 101 by determining the language of the userquery, e.g., English. It may parse the user query to extract verbs andnouns from the query, at step 102. The verbs generally relate to theactions that the user is interested in performing, and the nounsgenerally correspond to the objects involved (such as a particularproduct, situation, or a technology component). The natural languageprocessing layer 402 may ignore connecting words in the statement, e.g.,“about”, “in” and “by” (step 103). It may perform other tasks on theuser query such as text segmentation, tokenization, disambiguation,spell-checking and normalization, per step 104. These tasks werepreviously described with reference to FIG. 4.

The natural language processing layer 402 may identify entities (e.g.,key terms) and relationships in the user query, at step 105 (forexample, “failed”, “after”, and “installation”). If there are terms inthe user query that the natural language processing layer 402 does notrecognize, it may refer to a dictionary, thesaurus, or lexicon at step106 to help determine their meaning and the user's intent. Once thenatural language processing layer 402 has extracted relevant terms fromthe user query, it may forward these terms to an ontology analyzer (suchas ontology layer 407) for analyzing a domain-specific ontology relatedto the user query, and matching the terms against concepts in theontology, per step 107.

FIG. 11 is a flowchart of an exemplary process that an ontology layer407 may follow for matching relevant terms from a user query againstconcepts and relationships in a domain-specific ontology. The processmay start at step 111 where an ontology concept matcher 408 matches atoken extracted from the user query to each concept in an ontology(e.g., a node in an ontology structure), and attributes andrelationships associated with the concept. Attributes may includesub-components, acronyms, and synonyms of the concept. If there is amatch between the token and a concept in the ontology, the naturallanguage processing layer 402 may annotate the token with the matchingconcept and its associated pillars, per step 112. The associated pillarsmay include situations, activities, products, IT components, etc., thatmirror requirements of a “perfect” query.

If the ontology concept matcher 408 identifies a partial match between atoken and an ontology concept, the natural language processing layer 402may annotate the matching token, and if necessary the query logic system400 may confirm the partial ontology match with the user, per step 113.In case the ontology concept matcher 408 identifies multiple ontologyconcepts that match a token, the query logic system 400 may ask the userto clarify and select the best ontology match through questions for theuser, at step 114. The user can choose the correct words based on thecontext, pillar and description of the matching token and ontologyconcepts, per step 115.

The query logic system 400 may perform the process illustrated in FIG.11 for each token extracted from the user query and may iterativelyrefine the user query based on concepts and relationships from theontology, with the goal of increasing the relevance of search results.Based on the analysis of the ontology, the ontology concept matcher 408may output a set of terms from the ontology, and their relationships,that match the relevant substrings extracted from the user query. Theontology layer 407 may provide the matched terms and relationships thequery processing layer 416 for continued processing by the query logicsystem 400, at step 116.

As described above with reference to FIG. 4, the ontology layer 407 maycomprise an ontology builder 410 for updating a domain ontology withterms and relationships that the query logic system 400 identifies whileprocessing user queries, per step 117. The ontology builder 410 thusextends the ontology and refines its contents over time in terms ofcompleteness and accuracy, based on actual user needs and problems andinformation identified in response to user queries. Once theontology-matching process is completed, the query logic system 400 mayforward matched ontology concepts to a query processing layer 416 torefine the query with additional user input if necessary.

FIG. 12 is a flowchart of an exemplary query refining process that aquery processing layer 416 may follow to determine the completeness andspecificity of a user query. The process may refine the query withadditional user questions and answers, and determine follow-on useractions. A query processing layer 416 in the query logic system 400 maystart at step 121 to determine whether ontology matches for a userquery, as returned from an ontology concept matcher 408, are specificenough for a search of related information. If the matches are notsufficiently specific, the question and answer processor 413 maygenerate and ask the user additional questions to clarify the user'sneed or problem, as previously described with reference to FIG. 4.

The query processing layer 416 may further determine whether the userquery is sufficiently complete for processing, per step 122. Forexample, the completeness checker 412 of the query logic system 400 maydetermine whether each part of a “perfect” query is present in the userquery. For any missing key descriptors, the query logic system 400 mayask the user additional questions with the goal of satisfying each partof a “perfect” query, at step 123. The query processing layer 416 mayrefine, at step 124, the specificity and completeness of the keydescriptors with the additional information that the user supplies inresponse to the questions.

Thus in one embodiment, it is determined that the outputted terms andrelationships between the terms (from step 903 in FIG. 9) are notsufficiently specific to enable a search for requested information inthe user query after the user query is refined based on the outputtedterms and relationships between the terms. Accordingly, questions arepresented to the user pertinent to the outputted terms and relationshipsbetween the terms not being sufficiently specific. From the user'sresponse to the presented questions, clarification is received withrespect to a specificity of the outputted terms and relationshipsbetween the terms. Then the user query is modified, based on thereceived clarification, to enable the refined user query to besufficiently specific to enable a search for requested information inthe refined user query based on the outputted terms and relationshipsbetween the terms. In this embodiment, the refined user query, based onthe outputted terms and relationships between the terms, comprises themodified user query based on the received clarification.

Thus in one embodiment, it is determined that the user query is notsufficiently complete due to the user query not including sufficientinformation to enable an expert in a field pertinent to the user queryto respond to a request for information in the user query. Accordingly,questions are presented to the user pertinent to the user query notbeing sufficiently complete. From the user's response to the presentedquestions, information missing from the user query is received from theuser. Then the user query is modified, based on the received missinginformation, to enable the expert in the field to respond to the requestfor information in the user query. In this embodiment, the refined userquery, based on the outputted terms and relationships between the terms(from step 903 in FIG. 9), comprises the modified user query based onthe missing information.

At step 125, the query processing layer 416 may conclude, based oninformation extracted from the user query, that the user query is moresuitable for a service rather than a search for information, e.g., aproduct replacement due to a defect. In that case, the query logicsystem 400 may direct the user to a service handling system rather thancontinuing with the information search. Furthermore, if the queryprocessing layer 416 determines that user actions are needed toaccurately determine the user's need or problem, or relevantinformation, it may formulate follow-on actions and present them to theuser at step 126.

In case the query processing layer 416 determines that the user querydescriptors are sufficiently complete and specific, it may forward thedescriptors to the search layer 419 in the query logic system 400, atstep 127. The search layer 419 may use a search engine or data analyticsprogram 417 to search information sources 418 such as databases,intranets, or the Internet. The search engine or data analytics program417 may identify from the sources 418 information related to the userquery and return the identified information to the user through userinterface 302.

The subject matter described above is provided by way of illustrationonly and should not be construed as limiting. Various modifications andsubstitutions of the described components and operations can be made bythose skilled in the art without departing from the spirit and scope ofthe disclosure defined in the following claims, the scope of which is tobe accorded the broadest interpretation so as to encompass suchmodifications and equivalent structures. As will be appreciated by thoseskilled in the art, the systems, methods, and procedures describedherein can be embodied in a programmable computer, computer executablesoftware, or digital circuitry. The software can be stored on computerreadable physically tangible media or hardware devices such aspersistent storage 213 (see FIG. 2) such as a floppy disk, ROM, harddisk, removable media, flash memory, a “memory stick”, optical media,magneto-optical media, CD-ROM, etc.

Accordingly, aspects of the present invention may take the form of anentirely hardware embodiment, an entirely software embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” Furthermore,aspects of the present invention may take the form of a computer programproduct comprising one or more physically tangible (e.g., hardware)computer readable medium(s) or devices having computer readable programcode stored therein, said program code configured to be executed by aprocessor of a computer system to implement the methods of the presentinvention. In one embodiment, the physically tangible computer readablemedium(s) and/or device(s) (e.g., hardware media and/or devices) thatstore said program code which implement methods of the present inventiondo not comprise a signal generally, or a transitory signal inparticular.

Any combination of one or more computer readable medium(s) or devicesmay be utilized. The computer readable medium may be a computer readablesignal medium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium or device may include the following: an electricalconnection having one or more wires, a portable computer diskette, ahard disk, a random access memory (RAM), a read-only memory (ROM), anerasable programmable read-only memory (EPROM or Flash memory), anoptical fiber, a portable compact disc read-only memory (CD-ROM), anoptical storage device, a magnetic storage device, or any suitablecombination of the foregoing. In the context of this document, acomputer readable storage medium may be any physically tangible mediumor hardware device that can contain or store a program for use by or inconnection with an instruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thedisclosure may be written in any combination of one or more programminglanguages, including an object oriented programming language such asJava, Smalltalk, C++ or the like and conventional procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The program code may execute entirely on the user's computer,partly on the user's computer, as a stand-alone software package, partlyon the user's computer and partly on a remote computer or entirely onthe remote computer or server. In the latter scenario, the remotecomputer may be connected to the user's computer through any type ofnetwork, including a local area network (LAN) or a wide area network(WAN), or the connection may be made to an external computer (forexample, through the Internet using an Internet Service Provider).

Aspects of the disclosure are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the figures described aboveillustrate the architecture, functionality, and operation of possibleimplementations of systems, methods and computer program productsaccording to various embodiments of the disclosure. In this regard, eachblock in the flowchart or block diagrams may represent a module,segment, or portion of code, which comprises one or more executableinstructions for implementing the specified logical function(s). Itshould also be noted that, in some alternative implementations, thefunctions noted in the block may occur out of the order noted in thefigures. For example, two blocks shown in succession may, in fact, beexecuted substantially concurrently, or the blocks may sometimes beexecuted in the reverse order, depending upon the functionalityinvolved. It will also be noted that each block of the block diagramsand/or flowchart illustration, and combinations of blocks in the blockdiagrams and/or flowchart illustration, can be implemented by specialpurpose hardware-based systems that perform the specified functions oracts, or combinations of special purpose hardware and computerinstructions.

While particular embodiments of the present invention have beendescribed herein for purposes of illustration, many modifications andchanges will become apparent to those skilled in the art. Accordingly,the appended claims are intended to encompass all such modifications andchanges as fall within the true spirit and scope of this invention.

1. A method for providing information to a user in response to areceived user query, said method comprising: a processor of a computersystem receiving the user query from the user, said received user queryexpressed in a free-form text format; said processor performing anatural language analysis to generate substrings relevant to thereceived user query; after said performing the natural languageanalysis, said processor performing an ontology analysis to output termsof an ontology of domain-specific information and to further outputrelationships between pairs of said terms, said outputted termsconstrained to match the relevant substrings generated by saidperforming the natural language analysis; after said performing theontology analysis, said processor performing a query analysis to analyzethe user query with respect to the outputted terms and relationshipsbetween the terms, said performing the query analysis comprisingascertaining whether the user query is more suitable for service thanfor a search for information; if said ascertaining ascertains that theuser query is more suitable for service than for said search forinformation, then identifying, to the user, service actions to beperformed by the user; if said ascertaining ascertains that the userquery is not more suitable for service than for said search forinformation, then refining the user query based on the outputted termsand relationships between the terms, generating a search query based onthe refined user query, initiating a search based on the search query,and providing results of the search to the user.
 2. The method of claim1, wherein said ascertaining ascertains that the user query is moresuitable for service than for said search for information.
 3. The methodof claim 1, wherein said ascertaining ascertains that the user query isnot more suitable for service than for said search for information. 4.The method of claim 3, wherein said performing the query analysiscomprises prior to said ascertaining: determining that the outputtedterms and relationships between the terms are not sufficiently specificto enable a search for requested information in the user query after theuser query is refined based on the outputted terms and relationshipsbetween the terms; presenting questions to the user pertinent to theoutputted terms and relationships between the terms not beingsufficiently specific; receiving, from the user in response to thepresented questions, clarification with respect to a specificity of theoutputted terms and relationships between the terms; and modifying theuser query, based on the received clarification, to enable the refineduser query to be sufficiently specific to enable said search forrequested information in the refined user query, wherein said refiningthe user query comprises refining the modified use query.
 5. The methodof claim 3, wherein said performing the query analysis comprises priorto said ascertaining: determining that the user query is notsufficiently complete due to the user query not including sufficientinformation to enable an expert in a field pertinent to the user queryto respond to a request for information in the user query; presentingquestions to the user pertinent to the user query not being sufficientlycomplete; receiving, from the user in response to the presentedquestions, information missing from the user query; and modifying theuser query, based on the received missing information, to make the userquery sufficiently complete to enable the expert in the field to respondto the request for information in the user query, wherein said refiningthe user query comprises refining the modified use query.
 6. The methodof claim 1, wherein the ontology is represented as a tree comprisingnodes and edges connecting adjacent nodes, wherein each node isassociated with a term of the ontology such that the nodes of the treecomprise the outputted terms that match the relevant substrings, andwherein each edge connecting adjacent nodes represents a relationshipbetween the terms to which the adjacent nodes are respectivelyassociated.
 7. The method of claim 1, wherein said performing theontology analysis comprises: identifying multiple terms of the ontologythat match a selected substring of the generated substrings; in responseto said identifying multiple terms, presenting questions to the user forassisting the user to select a term of the multiple terms; after saidpresenting questions to the user, receiving from the user a selectedterm of the multiple terms that best matches the selected substring. 8.The method of claim 1, wherein said performing the ontology analysiscomprises annotating a substring of the generated substrings with a termof the outputted terms.
 9. The method of claim 1, wherein saidperforming the natural language analysis comprises assigning alinguistic category to different words of the user query, wherein eachlinguistic category is specific to the word to which each linguisticcategory is assigned, and wherein generation of the substrings byperforming the natural language analysis comprises utilizing thelinguistic category assigned to the different words of the user query.10. The method of claim 9, wherein a first, second, and third linguisticcategory assigned to a first, second, and third word is a noun, a verb,and an adjective, respectively.
 11. The method of claim 1, wherein saidperforming the natural language analysis comprises performing amorphological analysis to determine a single word consisting of a lemmathat is specific to each word of a plurality of words of the user query,and wherein the generated substrings comprise the lemma specific to eachword of the plurality of words of the user query.